849 research outputs found
Learning to Read by Spelling: Towards Unsupervised Text Recognition
This work presents a method for visual text recognition without using any
paired supervisory data. We formulate the text recognition task as one of
aligning the conditional distribution of strings predicted from given text
images, with lexically valid strings sampled from target corpora. This enables
fully automated, and unsupervised learning from just line-level text-images,
and unpaired text-string samples, obviating the need for large aligned
datasets. We present detailed analysis for various aspects of the proposed
method, namely - (1) impact of the length of training sequences on convergence,
(2) relation between character frequencies and the order in which they are
learnt, (3) generalisation ability of our recognition network to inputs of
arbitrary lengths, and (4) impact of varying the text corpus on recognition
accuracy. Finally, we demonstrate excellent text recognition accuracy on both
synthetically generated text images, and scanned images of real printed books,
using no labelled training examples
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
Large language models (LLMs) such as ChatGPT have recently demonstrated
significant potential in mathematical abilities, providing valuable reasoning
paradigm consistent with human natural language. However, LLMs currently have
difficulty in bridging perception, language understanding and reasoning
capabilities due to incompatibility of the underlying information flow among
them, making it challenging to accomplish tasks autonomously. On the other
hand, abductive learning (ABL) frameworks for integrating the two abilities of
perception and reasoning has seen significant success in inverse decipherment
of incomplete facts, but it is limited by the lack of semantic understanding of
logical reasoning rules and the dependence on complicated domain knowledge
representation. This paper presents a novel method (ChatABL) for integrating
LLMs into the ABL framework, aiming at unifying the three abilities in a more
user-friendly and understandable manner. The proposed method uses the strengths
of LLMs' understanding and logical reasoning to correct the incomplete logical
facts for optimizing the performance of perceptual module, by summarizing and
reorganizing reasoning rules represented in natural language format. Similarly,
perceptual module provides necessary reasoning examples for LLMs in natural
language format. The variable-length handwritten equation deciphering task, an
abstract expression of the Mayan calendar decoding, is used as a testbed to
demonstrate that ChatABL has reasoning ability beyond most existing
state-of-the-art methods, which has been well supported by comparative studies.
To our best knowledge, the proposed ChatABL is the first attempt to explore a
new pattern for further approaching human-level cognitive ability via natural
language interaction with ChatGPT
An Effective Approach to Unsupervised Machine Translation
While machine translation has traditionally relied on large amounts of
parallel corpora, a recent research line has managed to train both Neural
Machine Translation (NMT) and Statistical Machine Translation (SMT) systems
using monolingual corpora only. In this paper, we identify and address several
deficiencies of existing unsupervised SMT approaches by exploiting subword
information, developing a theoretically well founded unsupervised tuning
method, and incorporating a joint refinement procedure. Moreover, we use our
improved SMT system to initialize a dual NMT model, which is further fine-tuned
through on-the-fly back-translation. Together, we obtain large improvements
over the previous state-of-the-art in unsupervised machine translation. For
instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points
more than the previous best unsupervised system, and 0.5 points more than the
(supervised) shared task winner back in 2014.Comment: ACL 201
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